This RMarkdown plots the output of the model fits and simulations
## mean se_mean sd 2.5% 25% 50%
## intercept 4.9584683 0.0023287928 0.06673754 4.8297127 4.9124222 4.9622167
## coef 0.6259157 0.0006562984 0.01817625 0.5937511 0.6130510 0.6254341
## sigma_vl 0.8785846 0.0009118262 0.02529317 0.8304077 0.8610963 0.8787208
## t_dof 8.7199841 0.0591645616 1.67096689 6.2193018 7.5693942 8.4754891
## 75% 97.5% n_eff Rhat
## intercept 5.0029611 5.0854325 821.2562 0.9993502
## coef 0.6381112 0.6619696 767.0189 1.0036362
## sigma_vl 0.8960365 0.9239442 769.4537 0.9997008
## t_dof 9.6492931 12.5993320 797.6500 0.9961905
## mean se_mean sd 2.5% 25% 50%
## intercept[1] 1.4740669 0.063600951 0.51426658 0.5547103 1.1022390 1.4413228
## intercept[2] 5.5697211 0.004700721 0.09439698 5.3836293 5.5095036 5.5661583
## coef[1] 0.2101603 0.004811855 0.04966600 0.1251691 0.1752057 0.2062638
## coef[2] 1.0539762 0.004750285 0.08053203 0.9250447 0.9975416 1.0441333
## sigma_vl 0.7198390 0.001096484 0.02706600 0.6701177 0.7004831 0.7205154
## t_dof 5.5544309 0.032311247 0.84426185 4.1477745 4.9488136 5.5054319
## 75% 97.5% n_eff Rhat
## intercept[1] 1.8125224 2.5715782 65.38067 1.061172
## intercept[2] 5.6317159 5.7583844 403.26198 1.015138
## coef[1] 0.2421099 0.3164470 106.53524 1.038743
## coef[2] 1.1016846 1.2390020 287.40740 1.022375
## sigma_vl 0.7382902 0.7759451 609.31709 1.002388
## t_dof 6.0392947 7.4199466 682.72670 1.000780
## There are a total of 280 infection episodes
## [1] 2 18
## [1] 8
There are multiple ways to define time to clearance. We use time to first CT value equal to 40.
mgcv
## Warning in predict.gam(mod_gam, data.frame(time = xs, ID = -1), re.effect = NA):
## factor levels -1 not in original fit
Individual fits data 1 - we pick the individuals with the most data